Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen

Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total n...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 17; číslo 10; s. 2252
Hlavní autoři: Jia, Shengyao, Li, Hongyang, Wang, Yanjie, Tong, Renyuan, Li, Qing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Switzerland MDPI AG 30.09.2017
MDPI
Témata:
Classification
data fusion
hyperspectral imaging
soil type classification
Soils
texture features
total nitrogen
data fusion
texture features
hyperspectral imaging
total nitrogen
soil type classification
ISSN:1424-8220, 1424-8220
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Shrnutí:Soil is an important environment for crop growth. Quick and accurately access to soil nutrient content information is a prerequisite for scientific fertilization. In this work, hyperspectral imaging (HSI) technology was applied for the classification of soil types and the measurement of soil total nitrogen (TN) content. A total of 183 soil samples collected from Shangyu City (People’s Republic of China), were scanned by a near-infrared hyperspectral imaging system with a wavelength range of 874–1734 nm. The soil samples belonged to three major soil types typical of this area, including paddy soil, red soil and seashore saline soil. The successive projections algorithm (SPA) method was utilized to select effective wavelengths from the full spectrum. Pattern texture features (energy, contrast, homogeneity and entropy) were extracted from the gray-scale images at the effective wavelengths. The support vector machines (SVM) and partial least squares regression (PLSR) methods were used to establish classification and prediction models, respectively. The results showed that by using the combined data sets of effective wavelengths and texture features for modelling an optimal correct classification rate of 91.8%. could be achieved. The soil samples were first classified, then the local models were established for soil TN according to soil types, which achieved better prediction results than the general models. The overall results indicated that hyperspectral imaging technology could be used for soil type classification and soil TN determination, and data fusion combining spectral and image texture information showed advantages for the classification of soil types.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s17102252